run_gec.py

#!/usr/bin/env python
# coding=utf-8
# Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for sequence to sequence.
"""
# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import numpy as np
from datasets import load_dataset, disable_caching, Dataset
import torch


import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
    DataCollatorForSeq2SeqGEC,
    MBartForConditionalGenerationGED,
    HfArgumentParser,
    M2M100Tokenizer,
    MBart50Tokenizer,
    MBart50TokenizerFast,
    MBartTokenizer,
    MBartTokenizerFast,
    Seq2SeqTrainer,
    Seq2SeqTrainingArguments,
    default_data_collator,
    set_seed
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version

from utils.postprocess import postprocess
import json


logger = logging.getLogger(__name__)

# A list of all multilingual tokenizer which require src_lang and tgt_lang attributes.
MULTILINGUAL_TOKENIZERS = [MBartTokenizer, MBartTokenizerFast, MBart50Tokenizer, MBart50TokenizerFast, M2M100Tokenizer]

torch.cuda.empty_cache()
disable_caching()


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """

    model_name_or_path: str = field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    tokenizer_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
    )
    use_fast_tokenizer: bool = field(
        default=True,
        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
    )


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    source_lang: str = field(default=None, metadata={"help": "Source language id for translation."})
    target_lang: str = field(default=None, metadata={"help": "Target language id for translation."})
    ged_tags: str = field(default=None, metadata={"help": "List of ged error tags labels."})
    preprocess_merges: bool = field(default=None, metadata={"help": "To preprocess Merges before inference."})

    dataset_name: Optional[str] = field(
        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
    )
    dataset_config_name: Optional[str] = field(
        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
    )
    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a jsonlines)."})
    validation_file: Optional[str] = field(
        default=None,
        metadata={
            "help": "An optional input evaluation data file to evaluate the metrics (sacrebleu) on a jsonlines file."
        },
    )
    test_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input test data file to evaluate the metrics (sacrebleu) on a jsonlines file."},
    )
    prediction_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional file to write the predictions to."},
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )
    max_source_length: Optional[int] = field(
        default=1024,
        metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },
    )
    max_target_length: Optional[int] = field(
        default=128,
        metadata={
            "help": (
                "The maximum total sequence length for target text after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },
    )
    val_max_target_length: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "The maximum total sequence length for validation target text after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
                "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
                "during ``evaluate`` and ``predict``."
            )
        },
    )
    pad_to_max_length: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether to pad all samples to model maximum sentence length. "
                "If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
                "efficient on GPU but very bad for TPU."
            )
        },
    )
    num_beams: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
                "which is used during ``evaluate`` and ``predict``."
            )
        },
    )
    num_return_sequences: Optional[int] = field(
        default=1,
        metadata={
            "help": (
                "Number of generated sequences. This argument will be passed to ``model.generate``, "
                "which is used during ``evaluate`` and ``predict``."
            )
        },
    )
    ignore_pad_token_for_loss: bool = field(
        default=True,
        metadata={
            "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
        },
    )
    source_prefix: Optional[str] = field(
        default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
    )
    forced_bos_token: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "The token to force as the first generated token after the :obj:`decoder_start_token_id`.Useful for"
                " multilingual models like :doc:`mBART <../model_doc/mbart>` where the first generated token needs to"
                " be the target language token.(Usually it is the target language token)"
            )
        },
    )

    def __post_init__(self):
        if self.val_max_target_length is None:
            self.val_max_target_length = self.max_target_length


def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_gec", model_args, data_args)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()


    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # preparing ged tags if the are provided
    if data_args.ged_tags:
        ged_labels = ['<pad>'] + get_labels(data_args.ged_tags)
        ged_id2label_map : Dict[int, str] = {i: label for i, label in
                                            enumerate(ged_labels)}
        ged_label2id_map : Dict[str, int] = {label: i for i, label in
                                            enumerate(ged_labels)}
        num_labels = len(ged_labels)



    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        cache_dir=None,
    )

    # Adding ged tags to config if they are provided
    if data_args.ged_tags:
        config.update({'ged_id2label': ged_id2label_map,
                       'ged_label2id': ged_label2id_map,
                       'ged_num_labels': num_labels
                       })

    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=None,
        use_fast=model_args.use_fast_tokenizer,
    )
    model = AutoModelForSeq2SeqLM.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=None,
    )

    model.resize_token_embeddings(len(tokenizer))

    # Set decoder_start_token_id
    if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
        if isinstance(tokenizer, MBartTokenizer):
            model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.target_lang]
        else:
            model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.target_lang)

    if model.config.decoder_start_token_id is None:
        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")

    prefix = data_args.source_prefix if data_args.source_prefix is not None else ""


    # For translation we set the codes of our source and target languages (only useful for mBART, the others will
    # ignore those attributes).
    if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)):
        assert data_args.target_lang is not None and data_args.source_lang is not None, (
            f"{tokenizer.__class__.__name__} is a multilingual tokenizer which requires --source_lang and "
            "--target_lang arguments."
        )

        tokenizer.src_lang = data_args.source_lang
        tokenizer.tgt_lang = data_args.target_lang

        # For multilingual translation models like mBART-50 and M2M100 we need to force the target language token
        # as the first generated token. We ask the user to explicitly provide this as --forced_bos_token argument.
        forced_bos_token_id = (
            tokenizer.lang_code_to_id[data_args.forced_bos_token] if data_args.forced_bos_token is not None else None
        )
        model.config.forced_bos_token_id = forced_bos_token_id

    # Get the language codes for input/target.
    source_lang = data_args.source_lang.split("_")[0]
    target_lang = data_args.target_lang.split("_")[0]

    # Temporarily set max_target_length for training.
    max_target_length = data_args.max_target_length
    padding = "max_length" if data_args.pad_to_max_length else False

    if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"):
        logger.warning(
            "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for"
            f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
        )

    def preprocess_function(examples):
        inputs = examples['raw']
        targets = examples['cor']
        ged_tags = examples['ged_tags']

        model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True)

        # Tokenize targets with the `text_target` keyword argument
        labels = tokenizer(text_target=targets, max_length=max_target_length, padding=padding, truncation=True)


        # Converting the ged labels to ids if tags were provided
        if data_args.ged_tags:
            features = featurize_ged(tokenizer, inputs, ged_tags, ged_label2id_map,
                                     is_t5='t5' in model_args.model_name_or_path.lower(),
                                     do_preprocess=data_args.preprocess_merges)


            if data_args.preprocess_merges:
                input_ids = [features[i]['input_ids'] for i in range(len(features))]
                attention_mask = [features[i]['attention_mask'] for i in range(len(features))]

                model_inputs["input_ids"] = input_ids
                model_inputs["attention_mask"] = attention_mask

            else:

                # sanity checking if the featurization was done correctly
                gold_ids = [model_inputs['input_ids'][i] for i in range(len(model_inputs['input_ids']))]
                gold_ids_check = [features[i]['input_ids'] for i in range(len(features))]

                attention_mask = [model_inputs['attention_mask'][i] for i in range(len(model_inputs['attention_mask']))]
                attention_mask_check = [features[i]['attention_mask'] for i in range(len(features))]

                assert gold_ids_check == gold_ids
                assert attention_mask == attention_mask_check


            model_inputs["ged_tags"] = [features[i]['ged_labels_ids']
                                          for i in range(len(features))]


        # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
        # padding in the loss.
        if padding == "max_length" and data_args.ignore_pad_token_for_loss:
            labels["input_ids"] = [
                [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
            ]

        model_inputs["labels"] = labels["input_ids"]

        return model_inputs


    if training_args.do_train:
        with open(data_args.train_file) as f:
            raw_data = [json.loads(l) for l in f.readlines()]

        dataset_dict = {'raw': [ex['raw'] for ex in raw_data],
                        'cor':  [ex['cor'] for ex in raw_data],
                        'ged_tags':  [ex['ged_tags'] for ex in raw_data]
                        }

        train_dataset = Dataset.from_dict(dataset_dict)
        column_names = train_dataset.column_names


        with training_args.main_process_first(desc="train dataset map pre-processing"):
            train_dataset = train_dataset.map(
                preprocess_function,
                batched=True,
                num_proc=data_args.preprocessing_num_workers,
                remove_columns=column_names,
                load_from_cache_file=False,
                desc="Running tokenizer on train dataset",
            )

    if training_args.do_predict:
        max_target_length = data_args.val_max_target_length
        with open(data_args.test_file) as f:
            raw_data = [json.loads(l) for l in f.readlines()]

        dataset_dict = {'raw': [ex['raw'] for ex in raw_data],
                        'cor':  [ex['cor'] for ex in raw_data],
                        'ged_tags':  [ex['ged_tags'] for ex in raw_data]
                        }

        predict_dataset = Dataset.from_dict(dataset_dict)
        raw_predict_dataset = Dataset.from_dict(dataset_dict)
        column_names = train_dataset.column_names

        with training_args.main_process_first(desc="prediction dataset map pre-processing"):
            predict_dataset = predict_dataset.map(
                preprocess_function,
                batched=True,
                num_proc=data_args.preprocessing_num_workers,
                remove_columns=column_names,
                load_from_cache_file=False,
                desc="Running tokenizer on prediction dataset",
            )


    # Data collator
    label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
    if data_args.pad_to_max_length:
        data_collator = default_data_collator
    else:
        data_collator = DataCollatorForSeq2SeqGEC(
            tokenizer,
            model=model,
            label_pad_token_id=label_pad_token_id,
            pad_to_multiple_of=8 if training_args.fp16 else None,
        )


    # Initialize our Trainer
    trainer = Seq2SeqTrainer(
        model=model,
        # optim=optim,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        tokenizer=tokenizer,
        data_collator=data_collator
    )

    # Training
    if training_args.do_train:
        checkpoint = None
        if training_args.resume_from_checkpoint is not None:
            checkpoint = training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            checkpoint = last_checkpoint

        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        trainer.save_model()  # Saves the tokenizer too for easy upload

        metrics = train_result.metrics

        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    # Evaluation
    results = {}
    max_length = (
        training_args.generation_max_length
        if training_args.generation_max_length is not None
        else data_args.val_max_target_length
    )
    num_beams = data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
    num_return_sequences = data_args.num_return_sequences


    if training_args.do_predict:
        logger.info("*** Predict ***")
        predict_results = trainer.predict(
            predict_dataset, metric_key_prefix="predict", max_length=max_length, num_beams=num_beams,
            num_return_sequences=num_return_sequences
        )
        metrics = predict_results.metrics
        max_predict_samples = (
            data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
        )
        metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))

        trainer.log_metrics("predict", metrics)
        trainer.save_metrics("predict", metrics)

        if trainer.is_world_process_zero():
            if training_args.predict_with_generate:

                predictions = tokenizer.batch_decode(
                    predict_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=False
                )
                predictions = [pred.strip() for pred in predictions]
                output_prediction_file = os.path.join(training_args.output_dir, data_args.prediction_file)
                with open(output_prediction_file, "w", encoding="utf-8") as writer:
                    writer.write("\n".join(predictions))
                    writer.write("\n")


                # last steps for post_processing: pnx tokenization and m2 optim
                post_processed_sents = postprocess(src_sents=[ex['raw'] for ex in raw_predict_dataset],
                                                preds_sents=predictions)


                with open(output_prediction_file+'.pp', "w", encoding="utf-8") as writer:
                    writer.write("\n".join(post_processed_sents))
                    writer.write("\n")


    return results


def preprocess(words, labels):
    """
    Process words by solving merge errors
    """

    new_words = []
    new_labels = []

    i = 0
    while i < len(words):
        word = words[i]
        label = labels[i]

        # TODO: Sometimes we might see a single Merge-B label
        # that is not followed by Merge-I. We should handle those 
        # cases better

        if label == 'MERGE-B':
            new_word = []
            new_word.append(word)
            i += 1

            while  i < len(labels) and 'MERGE-I' in labels[i]:
                new_word.append(words[i])
                i += 1

            new_word = ''.join(new_word)
            new_words.append(new_word)
            new_labels.append('UC')

        elif label == 'DELETE':
            i += 1
            continue

        else:
            new_words.append(word)
            new_labels.append(label)
            i += 1

    assert len(new_words) == len(new_labels)

    return new_words, new_labels


def featurize_ged(tokenizer, inputs, labels, label_map, is_t5=False, do_preprocess=False):
    """
    Featurizes ged labels. Each subword that belongs to the same word
    gets the same ged label (i.e., each input sentence will have
    the same number of subwords and same number of ged labels)
    """

    features = []

    for i, (seq, seq_labels) in enumerate(zip(inputs, labels)):
        labels = []
        tokens = []

        words, ged_labels = seq.split(), seq_labels.split()
        assert len(words) == len(ged_labels)

        if do_preprocess:
            words, ged_labels = preprocess(words, ged_labels)

        for word, label in zip(words, ged_labels):
            word_tokens = tokenizer.tokenize(word)

            if len(word_tokens) > 0:
                tokens.extend(word_tokens)
                labels.extend([label for _ in range(len(word_tokens))])

        input_ids = tokenizer.convert_tokens_to_ids(tokens)
        # converting the labels to label ids
        # for labels that we have not seen in training, they get pad_id
        label_ids = [label_map.get(label, label_map['<pad>']) for label in labels]

        # Adding bos and eos tokens to the input ids
        if is_t5:
            input_ids = input_ids + [tokenizer.eos_token_id]
            label_ids = label_ids + [label_map['UC']]
        else:
            input_ids = [tokenizer.bos_token_id] + input_ids + [tokenizer.eos_token_id]
            label_ids = [label_map['UC']] + label_ids + [label_map['UC']]


        assert len(label_ids) == len(input_ids)

        attention_mask = [1 for _ in range(len(input_ids))]

        features.append({'input_ids': input_ids,
                         'ged_labels_ids': label_ids,
                         'attention_mask': attention_mask}
                        )

    return features


def get_labels(labels_path):
    with open(labels_path, mode='r', encoding='utf8') as f:
        labels = [l.strip() for l in f.readlines()]
    if 'UNK' in labels:
        labels.remove('UNK')
    return labels


def _mp_fn(index):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()